Genetic alterations and epigenetic modifications are hallmarks of cancers, including prostate cancer (PCa). Understanding factors associated with the progression of PCa will have a significant impact on management and treatment of the disease. We hypothesize that genetic changes in the germline or tumor genome, as well as epigenetic modifications, may independently or jointly affect the expression of the genes that are involved in the progression of PCa. To test our hypotheses and to uncover the genetic and epigenetic markers of the complex mechanisms underlining the progression and poor clinical outcome of PCa, we present specific aims with integrated and novel analytic approaches in this proposal. First, we will screen for germline DNA copy number variants (CNVs), somatic DNA copy number changes and modifications in methylation status across the whole genome among 200 intermediate- to high-grade PCa patients, with or without disease progression. Secondly, we will assess the roles of genetic and epigenetic changes in both germline and the tumor genomes in progression of PCa by comparing the frequencies of germline CNVs, somatic deletions and gains, and hyper- or hypomethylation between progressors and non-progressors. We expect to identify a number of alterations that are recurrent and have significantly higher frequencies among progressors. We will then select a sub set of the significant genetic and epigenetic alterations for validation using the DNA isolated from FFPE tissues and quantative PCR (qPCR), and bisulfite sequencing respectively. We will use Kappa statistics to measure the agreement of genetic/epigenetic alterations between fresh frozen and FFPE samples. The data generated and the molecular biomarkers identified from this pioneering study will provide potential targets in both germline and somatic genomes that may be related to PCa progression for further large-scale and multi-cohort validations using FFPE samples. The results of this study may advance our understanding on the etiology of PCa progression and augment current methods to better predict which PCa patient will likely develop progressed disease at the time of diagnosis. The Patients with poor prognosis can receive intensive monitor and treatment.